This digest introduces an Artificial Neural Network (ANN)-based solution for real-time monitoring and diagnostics of solar photovoltaic (PV) arrays, emphasizing the importance of fault detection, classification, and location in enhancing the reliability, performance, and safety of PV systems. The proposed system identifies and categorizes faults such as short circuits, open circuits, partial shading, and module degradation by analyzing electrical parameters like voltage, current, and power output under varying environmental conditions. Additionally, it accurately pinpoints fault locations, enabling swift corrective actions. By leveraging machine learning, the ANN model effectively identifies complex fault patterns, surpassing traditional thresholdbased techniques. This advanced fault diagnostic approach plays a crucial role in optimizing solar PV systems, promoting their sustainability and reliability as a renewable energy source.